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Harvesting Knowledge: An Innovative Ontology Framework for Agricultural Advancement using NPL methodologies

Harvesting Knowledge: An Innovative Ontology Framework for A...

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Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： Deepa R Manisha Das Thilakavathy P Bennet Prabhu A Surendran R Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai India Department of CSE(AI&ML) GMR Institute of Technology Rajam Andhrapradesh India Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India KONE Elevator India Technology & Engineering Centre Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369090

This paper introduces Agricultural Advancement using NLP (AA-NLP) techniques as a strategy to overcome the limitations of traditional agricultural management information systems. There is a lot of unstructured data available but this situation makes agricultural research inefficient because of its knowledge extraction and usage failure. This problem is addressed by combining an ontology framework developed for agricultural information management with Natural Language Processing (NLP) methods. The solution, AA-NLP, utilizes modern NLP algorithms which extract, classify and evaluate agricultural information from many textual sources that include research papers, reports and online repositories. AA-NLP achieves effective information retrieval and exploitation through named entity identification, sentiment analysis and semantic similarity as well as other techniques to convert unstructured input into structured ontological representations. Applying AA-NLP has several benefits such as increased discoverability of relevant agricultural information, automatic classification of scientific findings, and generation of actionable insights for decision making in farming practices, crop management and formulation of agricultural policies. The effectiveness of AA-NLP was analyzed in this paper by measuring the precision associated with knowledge extraction and ontology creation through various performance metrics including accuracy, sustainability farming practices, agricultural productivity and precision.

关键词： Productivity Sentiment analysis Semantics Ontologies Information retrieval Information management Data mining Stakeholders Sustainable development Farming

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Denial of Service Attack Analysis Using Machine Learning Techniques

Denial of Service Attack Analysis Using Machine Learning Tec...

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International Conference on Edge Computing and Applications (ICECAA)

作者： S.Faizal Mukthar Hussain R. Karthikeyan S. Ramamoorthi I.Sheik Arafat S.Syed Musthafa Gani Department of Computer Science and Engineering Mohamed Sathak Engineering College Kilakarai India Department of CSE (AI&ML) Vardhaman College of Engineering Hyderabad Telangana India Department of Electronics and Communication Engineering Sethu Institute of Technology Virudhunagar India Department of Electrical and Electronics Engineering Mohamed Sathak Engineering College Kilakarai India

Internet helps the users to develop their skills as well as for doing their day to day activities. Regular work of the users is generally done via online. If people want to do money based transaction or sending important message to their neighbor, they use online network. This can be used and interfere by the attackers. Many attackers utilize the tools to disturb the authorized user job. Major attack utilized by the attacker to disturb the service get by the authorized users is denial of service. It becomes major kind of procedure done by the intruder to take control over system or to down the server. In this paper we analyze the DoS attack including its distributed version. Impacts of DoS are also discussed. Based on the survey and analysis we suggest the model for handling the DoS attack dynamically is also mentioned.

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Revealing Confidential Information: The Function of Data Analytics for Participants in Contemporary Energy Internet

Revealing Confidential Information: The Function of Data Ana...

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Cognitive Computing in engineering, Communications, sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Subhash Bhagavan Kommina Shahnaz K V Gaddam Ashok Sruthi Yenninti G. Deepa Regonda Nagaraju Department of Information Technology Sasi Institute of Technology & Engineering Tadepalligudem AP India Department of ECE Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai TN India Department of Mechanical Engineering SR University Warangal Telangana India Department of CSE(AI&ML DS) Anil Neerukonda Institute of Technology and Science Visakhapatnam AP India Department of ECE Kongu Engineering College Erode TN India Department of AI & ML School of Engineering Malla Redy University Hyderabad Telangana India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

A new era of energy management with varied energy sources, changing customer wants, and environmental concerns has been brought about by the Energy Internet. In the uncertain world of today, data analytics aids in problem solving and opportunity seizing for all. The complexity of data analytics is examined in this essay along with its significance for Energy Internet workers. In this essay, the significance of data analytics for energy experts is emphasised. A novel method is created by combining Time Series Forecasting, Machine Learning for Anomaly Detection, and Predictive Analytics for Renewable Energy Integration. Combining these algorithms creates a data-driven system to address the main challenges facing the energy sector. To thoroughly examine the process, a comparative analysis was conducted. This study outlines the advantages and disadvantages of the suggested approach and compares it to corporate norms. The MAE, RMSE, F1-Score, Precision, and Recall metrics are used to gauge how effective certain methods are. According to the investigation, the suggested method achieves a reasonable balance between precise forecasts and outliers. It continuously enhances system stability, renewable energy use, and energy demand forecasting.

关键词： Renewable energy sources Energy consumption Data analysis Time series analysis Energy Internet Machine learning Stakeholders Problem-solving Forecasting Predictive analytics

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Optimizing Brain Tumor Segmentation with Advanced Superpixel Techniques in Conjunction with Deep Learning Methods

Optimizing Brain Tumor Segmentation with Advanced Superpixel...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Chythanya, Kanegonda Ravi Aruna, S. Karuppaiah, Jayakumar Arul, S. Benjamin Kumar Pareek, Piyush Sr University School of Computer Science & Artificial Intelligence Warangal India Srmist Dept. of Computational Intelligence Chennai India KIT- Kalaignarkarunanidhi Institute of Technology Department of Biomedical Engineering Coimbatore India School of Engineering and Technology Jeppiaar University Chennai India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350313413

Brain tumor segmentation in medical imaging is crucial for precise diagnosis and treatment planning. To address these issues, our research leverages deep learning techniques and introduces an innovative approach that combines various key components. Our methodology includes preprocessing, superpixel segmentation, feature extraction, and the use of a Convolutional Neural Network (CNN) to achieve precise brain tumor segmentation. This approach is driven by the necessity for improved accuracy and efficiency, especially with diverse clinical data and tumor types, like the challenging BraTS 2018 dataset, consisting of 1070 T1W-CE MRI images. This approach using metrics such as the Dice score, sensitivity, and specificity, which demonstrate the superior performance of our superpixel model in brain tumor segmentation. This proposed method yields exceptional results, with a Dice score of 97.06%, sensitivity of 99.05%, and specificity of 99.83%, highlighting its accuracy and effectiveness in tumor segmentation when compared to existing methods such as UNet, Kapur entropy, and rough set theory. This research significantly advances the state of the art and offers great promise for enhancing medical image analysis in this critical field. © 2023 IEEE.

关键词： Brain tumor Convolutional Neural Network Deep Learning Superpixel technique Tumor segmentation

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Fake News Detection using Machine Learning Algorithms

Fake News Detection using Machine Learning Algorithms

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New Frontiers in Communication, Automation, Management and Security (ICCAMS), International Conference on

作者： Tina Babu Rekha R Nair Adithya Challa Rahul Srikanth Sri Sai Aravindan Suhas S Department of Computer Science and Engineering Alliance Univerity Bengaluru India Department of Computer Science & Engineering (Data Science) School of Engineering Dayananda Sagar Univerity Bengaluru India Department of Computer Science & Engineering (AI & ML) School of Engineering Dayananda Sagar Univerity Bengaluru India

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

The spread of false news threatens news and information sources, especially in Indian politics. We present a machine learning-based strategy to identify false news by vectorizing and tokenizing news headlines using a pre-defined dataset of authentic and fraudulent news items. We want to create a model that can properly identify news stories by textual content, separating propaganda from real news. Our technique is tested on a benchmark dataset of news items. Our suggested technique outperforms various state-of-the-art false news detection algorithms in the literature. Our methodology can prevent false news and safeguard news and information sources in Indian politics. This technique is versatile and successful for identifying and reducing the impact of false news across several topics and languages.

关键词： Machine learning algorithms Limiting Detectors Forestry Graph theory Security Labeling

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Deployment Schemes of In-Band Management Mode Based on Industrial SDN Network

Deployment Schemes of In-Band Management Mode Based on Indus...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Mudit Mittal Hassan M. Al-Jawahry Neeraj Varshney Sunil Prashanth Kumar Jacob Michaelson R. Akhilesh Reddy Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Department of Computer Engineering and Applications GLA University Mathura Department of Mechanical Engineering New Horizon College of Engineering Bangalore Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

In order to properly manage and monitor their networks in industrial environments, industrial Software-Defined Networking (SDN) networks need to have an in-band administration mode. This research investigates a wide variety of alternative deployment options for in-band management mode in industrial SDN networks in order to increase the performance of the network, as well as its dependability and level of network security. We study how the performance of the network differs depending on whatever coupling of in-band management and industrial SDN architecture we choose. We analyse a range of potential pairings of in-band management with industrial SDN design. We also discuss the advantages and disadvantages of each deployment technique, providing insights into the creation and management of robust Industrial SDN networks. It provides useful recommendations for increasing the efficacy and security of particular network infrastructures, which might be valuable to both corporate groups and network administrators. The adoption of SDN technology and the installation of tailored management modes are becoming more and more significant as industrial settings continue to expand. This is of the utmost significance in order to guarantee the highest possible standards of both operational excellence and safety.

关键词： Surveys Reviews Scalability Quality of service Throughput Real-time systems Safety Software defined networking Standards Monitoring

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Integrating DCCS-Net for High-Fidelity Watermarking and Tampering Detection in Brain Scan Images

Integrating DCCS-Net for High-Fidelity Watermarking and Tamp...

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Cybernetics, Cognition and Machine Learning Applications (ICCCmlA), IEEE International Conference on

作者： Bsh. Shayeez Ahamed Radhika Baskar G Nalinipriya Department of Computer Science & Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of CSE-AI&ML Madanapalle Institute of Technology & Science Madanapalle Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Information Technology Saveetha Engineering College Chennai Tamilnadu India

ISBN: (数字)9798331505790

ISBN: (纸本)9798331505806

This study presents a new approach that combines the Deep Cascade Compressed Sensing Network (DCCS-Net) with a watermarking and tampering detection framework to improve the accuracy and quality of medical images. The system analyzes brain scan images by incorporating watermarks and identifying any tampering, thereby guaranteeing a high level of accuracy and maintaining the structural resemblance to the original images. The essential steps involve preprocessing the image using Discrete Cosine Transform (DCT), employing compressed sensing for efficient data management, and embedding watermarks using binary quantified reference values. The evaluation metrics, namely Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM), indicate that the watermarked images exhibit a PSNR of 41.906 and SSIM of 0.98458. These values suggest that there is minimal degradation in the quality of the images. The tampering detection process achieves a precision of 0.94531, effectively identifying tampered blocks. The reconstructed images exhibit minimal degradation in quality, as evidenced by a PSNR of 36.21 and SSIM of 0.9789. The results highlight the ability of DCCS-Net to effectively manage noise, minimize artifacts, and maintain intricate details, thereby improving the accuracy and reliability of diagnostics. The graphical depiction of metrics provides additional evidence to support the strength and reliability of the methodology in preserving image quality and reconstructing manipulated images, thus ensuring the trustworthiness and reliability of medical image analysis.

关键词： Measurement Accuracy PSNR Noise Watermarking Reliability Discrete cosine transforms Image reconstruction Medical diagnostic imaging Compressed sensing

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Optimizing Regulatory Compliance in Supply Chains with Blockchain and Gaussian Process Regression

Optimizing Regulatory Compliance in Supply Chains with Block...

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IoT, Communication and Automation Technology (ICICAT), International Conference on

作者： D. Tulasiram S. Kanmani Jebaseeli Myasar M. Adnan Arelli Madhavi Neeraj Varshney Nandhini T J Department of CSE CMR College of Engineering & Technology Department of IT New Prince Shri Bhavani College of Engineering and Technology Chennai Tamil Nadu India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of AI&ML GRIET Bachupally Hyderabad Telangana India Department of Computer Engineering & Applications GLA University Mathura Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Science - (SIMATS) Saveetha University Chennai India

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

Increased regulation of the supply chain is another important issue of concern to industries in distinct countries as they struggle to implement the regulation in a network setting. This paper aims to explore ways to improve regulatory compliance through the application of the blockchain and machine learning model, namely Gaussian process regression. Blockchain is an accurate and permanent record-keeping system that can be applied to track the flow of items and associated activities in the supply chain. Compliance can also be enforced, and through smart contracts, analyses of various parameters such as obligations can be monitored and adjusted automatically through GPR which involves the analysis of data from systems that use blockchain to model compliance risks and predict compliance issues. Thus, using the above-mentioned technologies, a company can prevent compliance failures, meet the necessary standards and set up a constant process of improvement in the supply chain area. This research specifically defines the theoretical foundations, application, and advantages of blockchain and GPR in enhancing the compliance of supply chains with the regulations in this work. In this approach, case-study and use of examples show how this approach works in handling compliance issues in several industries.

关键词： Industries Supply chains Smart contracts Gaussian processes Companies Predictive models Regulation Real-time systems Data models Blockchains

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Improving Support Vector Machine Performance with Advanced Kernel Methods

Improving Support Vector Machine Performance with Advanced K...

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International Conference on Contemporary Computing and Informatics (IC3I)

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

In many different fields, Support Vector Machines (SVMs) have shown to be an effective tool for regression and classification problems. When using support vector machines (SVMs), the kernel function—which specifies the feature space where the data is linearly separable—has a significant impact on the performance of the model. In this research, we look at how to improve SVM performance by creating and using new kernel approaches. We contrast the effectiveness of conventional kernels like the linear, polynomial, and radial basis function (RBF) kernels with new kernel functions designed to capture complicated data structures. Extensive studies on benchmark datasets show that classification accuracy, convergence speed, and generalisability are significantly improved. Applications in image recognition, bioinformatics, and financial forecasting are well-suited to the suggested kernels because to their exceptional performance in dealing with high-dimensional and non-linearly separable data. Research and practitioners in the area may benefit greatly from this study’s thorough examination of the computational complexity and actual implementation of these sophisticated kernel approaches. It offers useful insights for optimising SVM performance.

关键词： Support vector machines Training Accuracy Systematics Polynomials Real-time systems Convolutional neural networks Kernel Tuning Optimization

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Skeletal Fragility Detection using Deep Learning

Skeletal Fragility Detection using Deep Learning

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International Conference on Inventive Computation Technologies (ICICT)

作者： K. Rama Krishna Reddy V. Sravya Reddy V. Sriram Sathvik N. Muthukumaran Shaik Mahaboob Subhani Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana Department of ECE Centre for Computational Imaging and Machine Vision Sri Eshwar College of Engineering Coimbatore Tamil Nadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

In the field of diagnosing health there has been a shift, in focus from simply identifying bone fractures to a broader evaluation of skeletal fragility. Skeletal fragility involves not detecting fractures but also assessing the health and resilience of the skeletal system, which is crucial for preventing future fractures. While an automated network (DNN) model has achieved impressive accuracy of 92.44% in distinguishing between healthy and fractured bones iti s important to consider additional factors when measuring skeletal fragility. These supplementary factors include bone density, composition, and comprehensive markers of bone health like vitamin D levels. To create a framework for assessing skeletal fragility and not just bone fractures it is necessary to expand the model’s scope by incorporating these essential additional factors and indicators. This holistic approach has the potential to provide a nuanced perspective, on bone health and the likelihood of future fractures. Ultimately this can significantly improve care by facilitating measures.

关键词： Deep learning Image processing Noise Surgery Manuals Bones Data augmentation

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